Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita. By running head we will get the first six rows.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Q1. Why does it make sense to have a log10 scale on x axis? When the data covers a wide range of values it can be useful to use the log10 to scale the axes so that we do not just get a continuous scale.
Q2. What country is the richest in 1952 (far right on x axis)? Kuwait. I figured this out in the following:
gapminder %>%
select(country, year, gdpPercap) %>% #here I select that I want to see country, year and gdpPercap
filter(year == "1952") %>% #selecting that I only want the year 1952 to be shown
arrange(desc(gdpPercap)) #arranging the order of gdpPercap to be descending
## # A tibble: 142 x 3
## country year gdpPercap
## <fct> <int> <dbl>
## 1 Kuwait 1952 108382.
## 2 Switzerland 1952 14734.
## 3 United States 1952 13990.
## 4 Canada 1952 11367.
## 5 New Zealand 1952 10557.
## 6 Norway 1952 10095.
## 7 Australia 1952 10040.
## 8 United Kingdom 1952 9980.
## 9 Bahrain 1952 9867.
## 10 Denmark 1952 9692.
## # … with 132 more rows
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
geom_point(aes(color = continent)) + #changing the color of the points according to continent
xlab("GDP Per Capita") + #changing the name of the x axis
ylab("Life Expectancy") #changing the name of the y axis
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Q3. Can you differentiate the continents by color and fix the axis labels? Yes I did it (see the above)
Q4. What are the five richest countries in the world in 2007? Norway, Kuwait, Singapore, United States and Ireland. I figured it out by doing the following:
gapminder %>%
select(country, year, gdpPercap) %>% #here I select that I want to see country, year and gdpPercap
filter(year == "2007") %>% #selecting that I only want the year 2007 to be shown
arrange(desc(gdpPercap)) #arranging the order of gdpPercap to be descending
## # A tibble: 142 x 3
## country year gdpPercap
## <fct> <int> <dbl>
## 1 Norway 2007 49357.
## 2 Kuwait 2007 47307.
## 3 Singapore 2007 47143.
## 4 United States 2007 42952.
## 5 Ireland 2007 40676.
## 6 Hong Kong, China 2007 39725.
## 7 Switzerland 2007 37506.
## 8 Netherlands 2007 36798.
## 9 Canada 2007 36319.
## 10 Iceland 2007 36181.
## # … with 132 more rows
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
library(gifski)
anim + transition_states(year,
transition_length = 1,
state_length = 1)
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]s I am going to add a title to the last of the animations, the one called anim2, by using the labs (labels) function and defining the title to be year.
anim2 +
labs(title = "year: {frame_time}") + #making the year change in sync with the animation
geom_point(aes(color = continent)) #coloring the animation by continent
Q6 Can you make the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]
I change the labels on the axes by disabling scientific notation:
options(scipen = 999) #disabling scientific notation, options changes how R visualizes data
Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
How is the life expectancy evolving in Denmark through time?
DK <- gapminder %>%
filter(country == "Denmark") #defining DK
DK
## # A tibble: 12 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Denmark Europe 1952 70.8 4334000 9692.
## 2 Denmark Europe 1957 71.8 4487831 11100.
## 3 Denmark Europe 1962 72.4 4646899 13583.
## 4 Denmark Europe 1967 73.0 4838800 15937.
## 5 Denmark Europe 1972 73.5 4991596 18866.
## 6 Denmark Europe 1977 74.7 5088419 20423.
## 7 Denmark Europe 1982 74.6 5117810 21688.
## 8 Denmark Europe 1987 74.8 5127024 25116.
## 9 Denmark Europe 1992 75.3 5171393 26407.
## 10 Denmark Europe 1997 76.1 5283663 29804.
## 11 Denmark Europe 2002 77.2 5374693 32167.
## 12 Denmark Europe 2007 78.3 5468120 35278.
ggplot(DK, aes(gdpPercap, lifeExp, group = country)) + #plotting gdp in x and lifeexp in y, grouping by country
geom_line() + #making the visualization a line
scale_color_viridis_d() +
labs(x = "GDP", y = "Life Expectancy") + #defining x and y
transition_reveal(year) #making it move
From the visualization we can see that the life expectancy in Denmark generally gets higher through the years and so does the GDP.